EquiformerV3: Scaling SE(3)-Equivariant Graph Transformers
EquiformerV3 scales efficient, expressive, general SE(3)-equivariant graph attention transformers:
- Efficient for practical 3D deployment
-...

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EquiformerV3 scales efficient, expressive, general SE(3)-equivariant graph attention transformers:
Adam's Law breakthrough in LLMs:
Efficient long-horizon agents trend accelerates:
Key highlights from Stanford HAI's 2026 AI Index for scientific AI progress:
Transformer attention trend: Heads connecting across layers.
De novo design breakthrough:
Synthesis hackathon winners prove why verifiable agents are essential for handling real value:
Edge inference win for space AI:
New paper offers a conditional analysis on optimization, data, and model capability to rethink generalization in reasoning SFT. Highlights core finetuning limits, guiding smarter RLHF and unlearning for product-scale reasoning.
Practical advances in paper curation tools:
Breakthrough in video-to-action world models: Adapts video generation to create a Neural Computer that simulates full OS interfaces from keystrokes,...
Emerging trend in self-improving agent architectures:
MegaStyle constructs diverse and scalable style datasets through consistent text-to-image style mapping, a breakthrough for vision dataset scaling in creative AI products enabling vast style generation.
UK AISI replicated Anthropic's steering to suppress evaluation awareness, with a shocking finding: 'control' vectors (books on shelves) match the impact of deliberate designs. Transparency risks loom for misalignment monitoring in AI products.